Risk management systems are commonly employed by financial institutions, resource-based corporations, trading organizations, governments, and other users, to make informed decisions in assessing and managing the risk associated with the operations of those users.
Within the field of risk management, an important factor in successfully managing financial risks and rewards within financial institutions is the effective management of credit risk. Many financial institutions originate and manage a substantial amount of credit risky assets. Wholesale bank loans, corporate bonds and credit derivatives together account for a significant amount of credit exposure in financial institutions worldwide.
Several risk management functions are used to support the measurement and management of credit risk, which typically include (1) assessing obligor (borrower) creditworthiness; (2) analyzing, structuring, valuing and pricing individual credit instruments; (3) measuring and controlling counter-party credit exposures; and (4) measuring and optimizing credit risk across credit portfolios. Within an overall enterprise credit risk framework, the functions of pricing and valuation of credit instruments are particularly important. The framework should support risk/reward analysis during the pre-deal credit origination process, ongoing mark-to-market monitoring of credit positions, and aggregate portfolio analysis.
Various models have been developed and used in prior art credit instrument valuation and pricing systems, however many of these models are applicable only to traded instruments such as corporate bonds and mortgages. For example, some prior art systems have been designed to model and value credit instruments of interest in a portfolio, where the value of each credit instrument under various scenarios is to be determined in a simulation. However, these prior art systems often utilize simplified valuation models for the credit instruments of interest, in which certain assumptions are made for ease of computation, but which do not accurately reflect the complex structure of some credit instruments. As a further example, no-arbitrage pricing techniques have been used in derivative valuation pricing systems since the early 1970s. These techniques as used in prior art pricing systems, however, they have been primarily applied only to traded instruments, and not to non-traded credit instruments such as, for example, corporate and commercial loans.
Credit models that have been described in prior publications may be broadly classified into two main categories, often referred to as the “structural” approach, and the “reduced-form” or “intensity-based” approach. These approaches as described in prior publications are well known in the art; for example, as described in Cossin et al., Advanced Credit Risk Analysis, (London: Wiley & Sons), 2001. However, these prior publications do not teach how such approaches are to be applied to accurately price complex non-traded credit instruments, such as loans. In particular, they do not discuss the details of the underlying financial options embedded in those structures, how to model and generate their future cash flows and how to apply those credit models for their valuation or to manage their risk.
Many prior art credit pricing models have dealt only minimally with the pricing and valuation of loans. Loans are typically complicated, custom-structured credit instruments, with state-contingent cash flow structures that vary with changes in the creditworthiness of a non-defaulting borrower (i.e. movements between various credit ratings short of default). The development of effective credit risk pricing models for loans has been slow. While a model having broad applicability is generally desirable, the need to model a substantial number of key product-specific features of loans in detail has made the development of such a model difficult.
Currently, one of the most prevalent methods used in practice for pricing and managing non-traded instruments such as loans applies the concept of RAROC (risk-adjusted return on capital). The RAROC approach attempts to distribute aggregate risk costs down to businesses, products, customers, and ultimately, individual transactions. Measures of static, marginal risk contributions are used in the RAROC approach to allocate capital costs directly to individual loans in relation to a firm's aggregate debt and equity costs. However, since RAROC is not a “no-arbitrage” technique, it does not reconcile the prices of loans with those of similar securities available in the market (such as bonds, other loans and credit derivatives). As a result, RAROC cannot assess comparative business opportunities and arbitrage-like situations arising from relative price mismatches. RAROC is also unable to capture the natural hedges that often motivate the creation of new credit securities.
Furthermore, implementations of the RAROC approach typically are subject to a number of limitations. For example, the approach neglects the state contingency of many loan cash flows, takes a static view of credit risk, generally considers an arbitrary fixed horizon in pricing credit risk, and uses highly subjective parameters in practice.
Financial institutions typically require detailed evaluations of the economic profitability of their bank lending operations, and accurate mark-to-market measures of investment portfolio performance. There is a need for more computationally efficient tools to support pre-deal loan structuring, and means to incorporate detailed mark-to-market valuation of non-traded loans into portfolio simulation models. Commercial loans and other credit instruments often include features such as prepayment rights, draw down options, pricing grids, and term-outs that cause the cash flows from the instruments to vary across variations in obligor credit worthiness. However, these features are not supported by many prior art credit instrument pricing and valuation systems. Corporate bonds and fixed-rate loans require models that measure both credit risk and interest rate risk, including embedded options that are subject to either form of risk. However, prior art credit instrument pricing systems have not assessed loan structures in complete detail, and do not provide computationally efficient and scalable solution algorithms which can be integrated with portfolio simulation and risk management capabilities of risk management systems. Furthermore, many prior art systems do not support the combined assessment of both credit risk and market risk where instruments contain substantial embedded options and structures and accordingly may not be able to price such instruments properly, nor can they support an integrated risk market and credit management solution.